期刊名称 | New Generation Computing NEW GENERAT COMPUT |
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期刊ISSN | 0288-3635 |
期刊官方网站 | https://www.springer.com/354 |
是否OA | No |
出版商 | Springer Japan |
出版周期 | Quarterly |
文章处理费 | 登录后查看 |
始发年份 | 1983 |
年文章数 | 34 |
最新影响因子 | 2.0(2023) scijournal影响因子 greensci影响因子 |
大类学科 | 小类学科 | Top | 综述 |
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工程技术4区 | COMPUTER SCIENCE, HARDWARE & ARCHITECTURE 计算机:硬件4区 | 否 | 否 |
COMPUTER SCIENCE, THEORY & METHODS 计算机:理论方法4区 |
CiteScore排名 | CiteScore | SJR | SNIP | ||
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学科 | 排名 | 百分位 | 5.9 | 0.560 | 1.016 |
Mathematics Theoretical Computer Science |
32/130 | 75% |
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Computer Science Computer Networks and Communications |
124/395 | 68% |
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Computer Science Hardware and Architecture |
59/177 | 66% |
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Computer Science Software |
144/407 | 64% |
自引率 | 5% |
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H-index | 25 |
SCI收录状况 |
Science Citation Index Expanded |
官方审稿时间 | 登录后查看 |
网友分享审稿时间 | 数据统计中,敬请期待。 |
接受率 | 登录后查看 |
PubMed Central (PMC) | http://www.ncbi.nlm.nih.gov/nlmcatalog?term=0288-3635%5BISSN%5D |
期刊投稿网址 | https://www.editorialmanager.com/ngco/ |
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收稿范围 | The journal is specially intended to support the development of new computational and cognitive paradigms stemming from the cross-fertilization of various research fields. These fields include, but are not limited to, programming (logic, constraint, functional, object-oriented), distributed/parallel computing, knowledge-based systems, agent-oriented systems, and cognitive aspects of human embodied knowledge. It also encourages theoretical and/or practical papers concerning all types of learning, knowledge discovery, evolutionary mechanisms, human cognition and learning, and emergent systems that can lead to key technologies enabling us to build more complex and intelligent systems. The editorial board hopes that New Generation Computing will work as a catalyst among active researchers with broad interests by ensuring a smooth publication process. Areas Learning: Foundations and Models of Learning, Computational Learning Theory, Grammatical Inference, Inductive Logic Programming, Statistical Learning Methods, Bayesian Networks, Reinforcement Learning Data Mining: Fundamental Data Mining Methods (e.g. Frequent Pattern Mining, Stream Data Mining, Graph and Network Mining, Relational Data Mining), Text and Web Mining, Statistical Methods for Data Mining, Machine Learning Methods for Data Mining, Visualization Methods for Data Mining, Practical Applications of Data Mining, Data Mining across Cyberspace and Real Space, Ethics of Data Mining (e.g. Bias, Fairness, Privacy, Social Acceptability), Data Mining to Solve Social Issues (e.g. Climate Change, Declining Birthrate and Aging Population, Cyber Warfare) Cognitive Computing: Modeling Human Knowledge, Modeling Human Problem Solving and Learning, Semantic Computing, Modeling and Analyzing Decision Making, Cognitive Architecture, Artificial General Intelligence, Human Level AI. Programming and Semantics: Foundations and Models of Computation, Computational Logic, Programming Systems, Declarative Programming, Concurrency and Parallelism, Quantum Computing. Control Theory of Bio- and Nano-systems: Formal Models of Molecular Systems, Computation by Token-based Systems, Non-Boolean Representations of Signals in Nature, Cellular Automata Based on Mechanisms Found in Nature. Bio/Nano/Molecular Computing and Engineering: Molecular Robotics & Artificial Cells, DNA Nanoengineering, Molecular Computing/Programming, Self-organizing Systems. Skill Science and Philosophy: Skills and Knowledge in Life, Communication and Social Skills, Learning of Embodied Skills and Knowledge, “Kansei" and Value Creation, Sports Science, Measurement and Analysis of Body Movements, Systems Theory of Body, Cognitive Approach of Skill Science, Subjective Verbalization of Proprioceptive Sense, Co-evolution of Body and Language, Symbol Grounding, Symbol Generation Computational Social Science: Social Media, Web Services, Web Mining, Social Studies, Semantic Web, Crowdsourcing, Social Systems, Social Simulation, Virtual Lab AI to Fight COVID-19 and Other Pandemics: Infectious Disease Forecasting including Effects of Confinements and Vaccination, AI for Increasing Epidemic Preparedness in Public Health, for the Detection of Diseases and in Genome Sequencing, Role of AI in Contact Tracing, AI-Assisted Testing, Generating Recommendations for Individuals' Health, Situation Awareness, Sentiment Analysis and Trustworthiness of Information during Epidemics |
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投稿指南 | https://link.springer.com/journal/354/submission-guidelines |
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